Role of peripheral endothelin receptors in an animal model of complex regional pain syndrome type 1 (CRPS-I)
Bibliographic record
Abstract
Chronic post-ischemic pain (CPIP) is an animal model of CRPS-I developed using a 3-h ischemia-reperfusion injury of the rodent hind paw. The contribution of local endothelin to nociception has been evaluated in CPIP mice by measuring sustained nociceptive behaviors (SNBs) following intraplantar injection of endothelin-1 or -2 (ET-1, ET-2). The effects of local BQ-123 (ETA-R antagonist), BQ-788 (ETB-R antagonist), IRL-1620 (ETB-R agonist) and naloxone (opioid antagonist) were assessed on ET-induced SNBs and/or mechanical and cold allodynia in CPIP mice. ETA-R and ETB-R expression was assessed using immunohistochemistry and Western blot analysis. Compared to shams, CPIP mice exhibited hypersensitivity to local ET-1 and ET-2. BQ-123 reduced ET-1- and ET-2-induced SNBs in both sham and CPIP animals, but not mechanical or cold allodynia. BQ-788 enhanced ET-1- and ET-2-induced SNBs in both sham and CPIP mice, and cold allodynia in CPIP mice. IRL-1620 displayed a non-opioid anti-nociceptive effect on ET-1- and ET-2-induced SNBs and mechanical allodynia in CPIP mice. The distribution of ETA-R and ETB-R was similar in plantar skin of sham and CPIP mice, but both receptors were over-expressed in plantar muscles of CPIP mice. This study shows that ETA-R and ETB-R have differing roles in nociception for sham and CPIP mice. CPIP mice exhibit more local endothelin-induced SNBs, develop a novel local ETB-R agonist-induced (non-opioid) analgesia, and exhibit over-expression of both receptors in plantar muscles, but not skin. The effectiveness of local ETB-R agonists as anti-allodynic treatments in CPIP mice holds promise for novel therapies in CRPS-I patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".